Name: Stéfano Terci Gasperazzo
Type: MSc dissertation
Publication date: 27/11/2014

Namesort ascending Role
Maria Cristina Rangel Co-advisor *
Maria Claudia Silva Boeres Advisor *

Examining board:

Namesort ascending Role
Maria Cristina Rangel Co advisor *
Maria Claudia Silva Boeres Advisor *
Luciana Salete Buriol External Examiner *
Claudine Santos Badue Internal Examiner *

Summary: Autonomous robots with the ability of planning their own way is a challenge that attracts many researchers in the area of robot navigation. In this context, this work aims to implement a hybrid PSO algorithm for planning paths in static environments for holonomic and non-holonomic vehicles. The proposed algorithm has two phases: the first uses A* algorithm to generates an initial and feasible trajectory which is optimized by the PSO algorithm in the second stage. Finally a post path planning phase can be applied in order to adapt it to non-holonomic vehicle kinematic constraints. The Ackerman model has been considered for the experiments. The Carnegie Mellon Robot Navigation
Toolkit (CARMEN) was used to perform the computational experiments considering five instances of maps artificially generated with obstacles. The performance of the A*PSO algorithm was compared with A*, PSO and A*-Hybrid State. The results of the dynamic instances were not compared with other algorithms. The computational results indicates that the algorithm A*PSO outperformes the PSO algorithm. With respect to the algorithm A*, the A*PSO achieved better solutions for 40% of the tested instances, but all of them,
with less waypoints. For non-holonomic instances, the A*PSO obtained longer paths, however smoother and safer.

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